class_docstr: Samples elements from [0, length) at fixed intervals.
  
  Parameters
  ----------
  length : int
      Length of the sequence.
  interval : int
      The number of items to skip between two samples.
  rollover : bool, default True
      Whether to start again from the first skipped item after reaching the end.
      If true, this sampler would start again from the first skipped item until all items
      are visited.
      Otherwise, iteration stops when end is reached and skipped items are ignored.
  
  Examples
  --------
  >>> sampler = contrib.data.IntervalSampler(13, interval=3)
  >>> list(sampler)
  [0, 3, 6, 9, 12, 1, 4, 7, 10, 2, 5, 8, 11]
  >>> sampler = contrib.data.IntervalSampler(13, interval=3, rollover=False)
  >>> list(sampler)
  [0, 3, 6, 9, 12]
code: def __init__(self, length, interval, rollover=True):
      assert (
          interval <= length
      ), "Interval {} must be smaller than or equal to length {}".format(
          interval, length
      )
      self._length = length
      self._interval = interval
      self._rollover = rollover
  
imports: import numpy
label_desc: Use image node_id 1 to create a new IntervalSampler object from inherited base classes: Sampler with example: obj = IntervalSampler(length, interval, rollover)